Slides: here
Videos: here
Optional extra readings:
CNNs:
https://towardsdatascience.com/gentle-dive-into-math-behind-convolutional-neural-networks-79a07dd44cf9
https://distill.pub/2017/feature-visualization/
https://towardsdatascience.com/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1
RNNs:
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
Recursive NNs:
https://nlp.stanford.edu/sentiment/treebank.html
Self-Attention Networks:
http://nlp.seas.harvard.edu/2018/04/03/attention.html
https://transformer.huggingface.co/
Weekly exercises